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Light Stripe Projection (LSP) Position Improvement by Using an Adaptive Filter Based on Polynomial and Parabola Fitting

机译:通过使用基于多项式和抛物线配件的自适应滤波器,光条纹投影(LSP)位置改进

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The problem of using light stripe projection (LSP) for 3D surface reconstruction is addressed in this paper. By using an adaptive filter, we show that we can recover 3D points that normally would go undetected due to light reflection and shape. Further, we show that the filter improves the accuracy of the 3D point coordinates. The filter is based on polynomial and parabola fitting. It generates a bounded polynomial or a smooth parabolic function based on a peak curve from a stripe sample and it adapts the smooth function so that other stripe sample pixels fit a new stripe sample. We then show how the filter is designed to correct the reflection affective pixels of a stripe sample and how it can improve the edge position extracted by any common edge detection method. The effectiveness of the missing 3D point recovery and the 3D point position accuracy improvement is demonstrated by the presentation of experimental results obtained using the methods described in the paper. A test demonstrating the differences between 3D point models generated with and without the adaptive filter is also presented.
机译:本文解决了用于3D表面重建的光条纹投影(LSP)的问题。通过使用自适应滤波器,我们表明我们可以恢复由于光反射和形状而通常不会被发现的3D点。此外,我们表明过滤器提高了3D点坐标的准确性。过滤器基于多项式和抛物线配件。它基于来自条纹样本的峰值曲线产生有界多项式或平滑抛物功能,并且它适应平滑功能,使得其他条纹样本像素适合新的条纹样本。然后,我们展示过滤器的设计如何校正条纹样本的反射情感像素以及如何改善由任何公共边缘检测方法提取的边缘位置。通过使用本文中描述的方法获得的实验结果表明,缺少3D点恢复和3D点位置精度改进的有效性。还呈现了演示使用和不使用自适应滤波器生成的3D点模型之间的差异的测试。

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